code: def forward(self, x, sPrev, yPrev):
      # x: feature sequence from the image decoder.
      batch_size, T, _ = x.shape
      alpha = self.attention_unit(x, sPrev)
      context = paddle.squeeze(
          paddle.matmul(alpha.unsqueeze(1), x), axis=1
      )
      yPrev = paddle.cast(yPrev, dtype="int64")
      yProj = self.tgt_embedding(yPrev)
  
      concat_context = paddle.concat([yProj, context], 1)
      concat_context = paddle.squeeze(concat_context, 1)
      sPrev = paddle.squeeze(sPrev, 0)
      output, state = self.gru(concat_context, sPrev)
      output = paddle.squeeze(output, axis=1)
      output = self.fc(output)
      return output, state
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import sys
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 3 for calling the DecoderUnit obj's underlying member method code with example usage: obj.forward(x, sPrev, yPrev) and returns: output,  state